The Application of Metabolomics in the Study of Pancreatitis
There are many causes of pancreatitis, the most common of which are bile duct obstruction caused by gallstone metastasis and pancreatic injury caused by excessive drinking. Despite the controversy, many scholars[23–25] still believe that the direct course of pancreatitis occurs due to the uncontrolled activation of trypsin in pancreatic acinar cells, which leads to the autodigestive inflammatory response of the pancreas. For example, trypsinase activates secondary inflammatory reactions that rupture a large number of cell membranes, and tetrahydropalmatine is an important component of cell membranes. In cell and serum experiments, the concentration of tetrahydropalmatine has been positively correlated with the severity of inflammatory reactions.[26–29]
Fatty acid metabolism should also be considered in the pathogenesis of AP. Disturbance of fatty acid metabolism can easily lead to hyperlipidemia, thus affecting the glycolysis pathway and causing hyperglycemia syndrome.[31–33] This series of inflammatory reactions will inevitably lead to changes in related metabolites.
CP is characterized by extensive and persistent fibrosis of the basic structure of the pancreas, a pathological process characterized by ductal changes, perilobular fibrosis of the pancreas, and eventual scarring of the pancreatic lobules.[34–36] The pathological and metabolic changes of CP coexist and develop simultaneously. Detection of metabolite levels may contribute to a better understanding of pathophysiological events and to the clinical treatment of diseases of the pancreas.
To date, only a few metabolomics studies have reported involvement with pancreatitis and related diseases. High-resolution proton magic angle spinning NMR spectroscopy is used to analyze the metabolites of acute necrotizing pancreatitis and CP in Wistar rats. In this experiment, it was found that inflammation of the pancreas can lead to changes in the corresponding metabolites (leucine-isoleucine-valine lipids and taurine); the authors suggested that examining the level of metabolites before pancreatic-tissue damage would contribute to better understanding of the underlying pathological and physiological conditions, thus contributing to the early diagnosis of pancreatitis. Gas chromatography–mass spectrometry (GC-MS) can then be used to analyze metabolite changes in patients with AP.
Other researchers have applied multivariate pattern-recognition technology to establish the classification of the models of APP and healthy participants to select 3-hydroxy butyric acid glycerol phosphate citric acid d-galactose d-mannose d-glucose palmitic acid serotonin and other important metabolites, such as potential biomarkers for clinical diagnosis of AP, and to analyze severe and mild symptoms in metabolite changes in patients with AP. Their results suggest that GC-MS–based serum metabolomics can be used for clinical diagnosis of AP by analyzing potential biomarkers.
In another study, serum metabolic profiles of patients with MAP, patients with cholelithiasis (CHO), and healthy volunteers were analyzed by ultra–high-performance liquid chromatography coupled with high-resolution mass spectrometry (UPLC-HRMS). These specific metabolites, which are potential biomarkers, provide a basis for monitoring and prognostic judgment in the diagnosis of AP. The results of this study found that with the progression of treatment, 4 metabolites, namely, Sphengani, L-thyroxine, acetylcholic acid, and tetradecanone, showed a gradual downward trend and gradually approached the normal level. Therefore, these entities can be used as metabolic biomarkers for AP clinical-process detection.
Tang et al used chemical derivatization and GC-MS to detect serum metabolites; they also used orthogonal projection latent structure discriminant analysis (OPLS-DA) for GC-MS data. Those data showed differences between healthy mice and mice with hyperlipidemic pancreatitis (HLP). Therefore, the researchers believe that this technique is a new and effective tool to study the pathogenesis of HLP.
Villaseñor et al investigated urinary and plasma metabolic phenotypes in AP using 1H NMR spectroscopy and multivariate modeling. According to the findings of their study, CHO and colitis in the nonpancreatitis group can also be distinguished by relevant metabolic phenotypes; those combined biomarkers play an important role in the diagnosis and prognosis of pancreatitis.
Sun et al, using a swine CP model with partial ligation of the main pancreatic duct (MPD), investigated the potential of metabolic markers obtained from pancreatic-tissue specimens with 1H NMR for CP diagnosed at different stages. Through their analysis, the potential of 1H NMR in the diagnosis of CP can be observed. The results of this study demonstrate the great potential of metabolic characteristics in differentiating healthy pancreatic tissue from tissue in different stages of CP, which may help in the early diagnosis of CP and thus guide clinicians to intervene in a timely manner, to prevent irreversible pancreatic injury.
Research by Lusczek et al shows that metabonomics technology can distinguish urine specimens collected from patients with pancreatitis and urine specimens collected from a healthy control group. This new, noninvasive technology can help clinicians to develop a thorough understanding of the metabolic state of patients with CP and AP. Lusczek et al reported that they are not sure whether these metabolites are biomarkers of AP; however, the methods described in their article present a strategy based on the premise that further analysis can help laboratory staff to test more of the specimens in queue. Also, a study using GC-MS–based metabolomics led to the discovery of candidate therapeutic agents for treatment of pancreatitis. The findings of these studies provide new ideas for the diagnosis and treatment of pancreatitis and further prove the feasibility of metabolomics in the diagnosis of pancreatitis.
Lab Med. 2020;51(2):116-121. © 2020 American Society for Clinical Pathology